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A critical step in achieving this goal is accurately identifying the categories of pathways in which a compound participated. However, current machine learning-based methods often overlook the multifaceted nature of compounds, resulting in inaccurate pathway predictions. Therefore, we present a novel framework on Multi-View Multi-Label Learning for Metabolic Pathway Inference, hereby named MVML-MPI. First, MVML-MPI learns the distinct compound representations in parallel with corresponding compound encoders to fully extract features. Subsequently, we propose an attention-based mechanism that offers a fusion module to complement these multi-view representations. As a result, MVML-MPI accurately represents and effectively captures the complex relationship between compounds and metabolic pathways and distinguishes itself from current machine learning-based methods. In experiments conducted on the Kyoto Encyclopedia of Genes and Genomes pathways dataset, MVML-MPI outperformed state-of-the-art methods, demonstrating the superiority of MVML-MPI and its potential to utilize the field of metabolic pathway design, which can aid in optimizing drug-like compounds and facilitating the development of GEMs. The code and data underlying this article are freely available at https:\/\/github.com\/guofei-tju\/MVML-MPI. Contact: \u00a0jtang@cse.sc.edu, guofei@csu.edu.com or wuxi_dyj@csj.uestc.edu.cn<\/jats:p>","DOI":"10.1093\/bib\/bbad393","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:10:22Z","timestamp":1699405822000},"source":"Crossref","is-referenced-by-count":15,"title":["MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference"],"prefix":"10.1093","volume":"24","author":[{"given":"Xiaoyi","family":"Liu","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina , Columbia 29208 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongpeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina , Columbia 29208 , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwei","family":"Ai","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijie","family":"Ding","sequence":"additional","affiliation":[{"name":"Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China , Quzhou 324000 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[{"name":"Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences , Nanshan 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,11,2]]},"reference":[{"issue":"8","key":"2023110801101066900_ref1","doi-asserted-by":"crossref","first-page":"935","DOI":"10.15252\/msb.20167411","article-title":"Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints","volume":"13","author":"S\u00e1nchez","year":"2017","journal-title":"Mol Syst Biol"},{"issue":"1","key":"2023110801101066900_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-25158-6","article-title":"A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics","volume":"12","author":"Oftadeh","year":"2021","journal-title":"Nat Commun"},{"issue":"46","key":"2023110801101066900_ref3","doi-asserted-by":"crossref","first-page":"e2211197119","DOI":"10.1073\/pnas.2211197119","article-title":"A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions","volume":"119","author":"Vayena","year":"2022","journal-title":"Proc Natl Acad Sci"},{"key":"2023110801101066900_ref4","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11780","article-title":"Beyond link prediction: Predicting hyperlinks in adjacency space","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Zhang","year":"2018"},{"issue":"1","key":"2023110801101066900_ref5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-11581-3","article-title":"A consensus S. 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